Marketing, Strategy, and The Force by Joe Winn

Tag: machine learning

“Ask Our Friendly AI!” Your credit union’s website is excitedly promoting their new chat bot, there to answer questions 24/7. “Cool, so how can it help me save money or time?” Whether they admit it or not, that’s what your members will be thinking. In some cases, such tech is fielding member requests without burdening traditional staff time. And their resolution rates can be similar to human representatives. What are you waiting for? Get Siri, Alexa, Cortana, and friends to every CU! (HAL is not welcome, sorry)

It’s not that simple. “AI” support agents are uniquely programmed to understand financial world terminology. Plus, computers don’t excel at interacting like a person, since we learn and process the world in a different way. One day, I’m certain this will no longer be the case, and all systems will talk to each other in the background, so you could ask Siri (remember that post?) to transfer money from one account to another, explain the tax implications of your specific IRA contributions, and what the score is for your favorite team. But we’re not at that point…yet. And look who spoke too soon…we’re actually getting awfully close.

Readers knowmypassion (that’s 3 links!) for the “AI Revolution”. With its arrival, a lot of ideas are being thrown around on best use. Right now, the most common answer is: Everywhere!!!

Patience, my young Padawan. A fancy chat bot might seem like the natural first step, but let’s look at it from a member benefit perspective. If they have a question, they don’t care who/what responds. They just want a quick and accurate answer. If your team is currently able to keep members served quickly and effectively (through any medium they contact), then this may not be a fit for you at this time. Unless you have unlimited resources, in which case, yes, do all of this at once. Just make sure you have top-notch project management to ensure the focus is always on the unified credit union goals.

For the rest of us, the AI which makes the most sense, if less “sexy”, is the Big Data side of AI, the machine learning. Here, you have solutions which can analyze a member’s credit (beyond the report) and offer a rapid loan decision with high rate accuracy. You can implement systems to monitor patterns in spending to identify fraud the moment it occurs, saving the institution money and the member frustration. Machine learning is also enabling security of the body, biometrics. You know it as the fingerprint sensor on your phone, but facial recognition is also commonplace on new Windows 10 computers, while retina scanners are the “top level” of security at large financial institutions.

Speed. Savings. Security. Three great reasons to implement aspects of AI in your credit union. A recent post about this topic ended with a wonderful quote:

“When a bank…effectively uses AI, they run more efficiently and are able to connect more effectively with a segment of the population that will never be replaced by machines: their customers.” – Mohit Joshi, Innovations in FinTech

Ok, ok. I’ve given you way too much to consider. AI, Big Data, machine learning, algorithmic analysis…yeah, I get it. Overwhelming when you just want to know, “can this stuff help my credit union?” So, I had a realization right after writing this post. Remember that series I did about tech in the financial industry? As part of it, I mentioned that financial institutions are at risk of becoming “dumb banks” in the same way that ISPs are “dumb pipes”, simply being the corridor for other companies’ information. You hold the money, but your members use other company services to move, spend, invest, even check on their funds. The same is the case here with AI.

There will always be a place for information as you manage it now: Raw account balances aren’t going anywhere. But that’s “dumb data”. The future is in “smart data”. Where your credit union and members can find patterns in spending, opportunities in lending, and personalized recommendations for minimizing debt (or maximizing wealth).

Tech itself is changing beneath our feet (and around us in “the cloud”)

For nearly all of us, the idea of technology in our lives revolves around things mentioned in the first post. What phone you use, the devices you connect to it, even your “smart home” accessories. It’s primarily the hardware, and, as we learned in the second post, the services you use on them. So, that’s the future: Ever-improving devices with more interesting apps.

Not quite. There’s an area of growth which seems so far-fetched that we discount it as “distant future”. But it’s here today.

Artificial intelligence.

We aren’t talking the adorable bots from *batteries not included, nor are we concerned with T-1000 units “terminating” their target. AI (or more accurately, machine learning) today is in some ways like a traffic light. It does one thing. However, unlike a traffic light, it’s always improving how it does that one thing. And you use these 1st generation AI systems everyday.

Your Facebook feed is a form of machine learning. It tailors posts shown based on what it learns you enjoy. The more you use it, the better it gets. Your iPhone keyboard is the same way. It actually adjusts the size and location of each key by tiny amounts as it learns where your fingers press most often. It even figures out how you talk to better predict the next word you’re going to write (and it knows whether you’re typing in a social or professional manner).

Search Google for the image of a cat. You just asked their machine learning system. Their latest endeavor is a platform called Deep Dream (caution: highly geek). Besides trippy imagery, it shows how a computer actually learns. Fascinating, as Data would say.

Interesting, but, once again…why? The first two parts related to what technology you use knowingly. Those spawn the interest in visible tech. Modern app platforms. Game-like member engagement. All great, and important. But it’s the machine learning which will offer the “just what I wanted” capability of future financial services.

Computers are smartest with tons of data. Big Data, you could say. With it, a learning system can figure out when a member is at risk of overdrawing their account or might be in the market for a car. How thrilled would they be if you could suggest adding overdraft protection an hour before they make a costly transaction? Or notify them of a great auto rate and car research system the day after their vehicle has engine troubles?

Unfortunately, I’m not smart enough to even offer the breadth of examples this future will offer. But I’ve read a lot from those who are. A recent CU Broadcast interview dove headfirst into the data side (without mentioning the AI part). Coastal CU does data analysis for member habits. Affinity CU just expressed interest in the concept after a short chat. And that’s just in the course of a few days. Much like winter, change is coming.

So how can you stay ahead of your competition while providing historically-great member service? Focus on what you do best, and find partners which excel in their complementary areas. By working together, perhaps we stand a chance against our computer overlords.

Today, this country made a decision, but it isn’t a decision in which anyone should be proud. Whether you voted for your candidate or against the other one, or a mixture of both (or went third-party), the conclusion is a troubling result. We’ve said that experience is irrelevant. We’ve said that decorum is unnecessary. We’ve rewarded hateful speech and actions, probably because we share the fears from which they derive. We’ve legitimized a lot more, but it’s not even worth diving into it here.

In my research of machine learning (artificial intelligence), a common theme arose around the idea of logarithmic change. This means that as change (in this case, computer performance and “smarts”) occurs, it occurs at a faster rate than prior. Not only does a system get smarter as it “grows”, but it gets smarter more quickly. Think of it like a car which goes 0-60 in 4 seconds, 60-120 in 3, 120-180 in 1, 180-240 in 0.1, and finally 240-300 in 0.0001 seconds. Once it’s going 500, can you even process that type of acceleration? More importantly, how would you describe the velocity increase at 1,000? If you’re struggling to wrap your brain around it, that’s ok. You’re not alone. We perceive the world linearly, and this is at the core of many challenges.

Our world has been in the midst of this increasing rate of change for all of its history. However, only within the past decade or so has it become so impactful on the average person’s life. Minorities are rapidly becoming the majority, social norms are shifting at an accelerated rate, and the divide between what our knowledge contains and what the average person knows (or even *could* know) is growing exponentially. You could probably describe the basic idea of how your VCR worked. How about your iPhone?

This is why the challenges of today (and tomorrow) are so difficult to reconcile. We think in a linear fashion: Last year was that, this year is such and such, so next year will be a derivative of those. Except this no longer applies. Change accelerated and next year will be something we can hardly imagine.

And neither candidate appeared to grasp this fundamental concept.

This election was an expression of deep-seated fear of the unknown (be it gay marriage, traditional gender roles breaking down, ethnic diversification on a majority scale, expanding capabilities of a surveillance state, and any number of other topics). What many always knew to be true simply isn’t anymore. Like being in an earthquake, people’s “bedrock” is cracking. Anxiety over what an ever-increasingly changing future will bring led Americans to make rash decisions all the way through the election process.

I don’t have any answers. I’m pretty sure our President-elect doesn’t, either. So we’re going to have to work together and figure out how we will move forward while navigating this wildly-accelerating car.